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Long-Term Memory

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Generalizable long short-term memory models for beef cattle DMI under grazing.

Nathan E Blake^1,2, K E ArunKumar^1,2, Matthew Walker^2,3,4

¹School of Agriculture and Food Systems, Davis College of Agriculture and Natural Resources, West Virginia University, Morgantown, WV, 26506, United States.

Journal of Animal Science

|May 11, 2026

Summary

We developed a novel sequence model to accurately predict daily dry matter intake (DMI) in cattle using easily obtainable data. This method significantly improves DMI estimation, especially for grazing animals, offering a scalable solution for evaluating feed efficiency.

Keywords:

beef cattle dry matter intake grazing long short-term memory temperature-humidity index water intake

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Area of Science:

Animal Science
Machine Learning
Agricultural Engineering

Background:

Estimating individual dry matter intake (DMI) in pasture-raised livestock is challenging due to high costs and labor requirements.
Current methods are often episodic, limiting continuous monitoring and accurate assessment of feed efficiency.

Purpose of the Study:

To develop a deployable sequence model for predicting daily, per-animal DMI using readily available data.
To evaluate the model's accuracy and generalizability across different livestock production systems, including grazing environments.

Main Methods:

A 3-layer long short-term memory (LSTM) network was employed, ingesting 7-day windows of engineered covariates derived from scale weights, water intake, animal metadata, and weather data.
The model incorporated rolling statistics, differences, lags, and Temperature-Humidity Index (THI), with categorical variables encoded using learned embeddings.
Training involved robust optimization techniques, hyperparameter tuning with Optuna, and evaluation against NASEM equations using external validation datasets.

Main Results:

The LSTM model demonstrated superior accuracy compared to NASEM equations, with a pooled RMSE of 1.329 kg/d versus 1.858 kg/d and an R² of 0.655 versus 0.326.
Significant improvements were observed in grazing systems, where the LSTM achieved an RMSE of 1.180 kg/d and an R² of 0.507, compared to NASEM's RMSE of 3.883 kg/d and R² of -4.337.
The model exhibited stable performance and generalizability across regional and non-regional drylot and grazing production systems.

Conclusions:

Accurate and generalizable DMI prediction is achievable using pragmatic, easily collected inputs.
The developed sequence model enables scalable evaluation of intake phenotypes and feed-efficiency traits directly within grazing systems.
This advancement facilitates more efficient livestock management and breeding programs focused on feed conversion.